Hi guys!

It’s really amazing what you can do within the R environment and one of the main desires of those who work with R is to make beautiful graphics. I speak for myself! For that, ggplot2 is an essential package for those who like to make beautiful graphics. But did you know that you can do more? Yes! You can create an interactive chart with any ggplot chart using the plotly package. Want to learn how to do? Then come with me!

Load packages and data frame

First, let’s start loading the necessary packages. We will use the gapminder package to obtain a database for our class.

library(ggplot2)
library(plotly)
library(gapminder)

Now let’s create our dataframe and visualize it.

df <- gapminder
df
## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # ... with 1,694 more rows

These Gapminder data are about life expectancy, GDP per capita and population by country, comprising several years. To facilitate our visualization in the graph, we will filter only the year 1992.

df = df%>%
  filter(year==1992)
df
## # A tibble: 142 x 6
##    country     continent  year lifeExp       pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Afghanistan Asia       1992    41.7  16317921      649.
##  2 Albania     Europe     1992    71.6   3326498     2497.
##  3 Algeria     Africa     1992    67.7  26298373     5023.
##  4 Angola      Africa     1992    40.6   8735988     2628.
##  5 Argentina   Americas   1992    71.9  33958947     9308.
##  6 Australia   Oceania    1992    77.6  17481977    23425.
##  7 Austria     Europe     1992    76.0   7914969    27042.
##  8 Bahrain     Asia       1992    72.6    529491    19036.
##  9 Bangladesh  Asia       1992    56.0 113704579      838.
## 10 Belgium     Europe     1992    76.5  10045622    25576.
## # ... with 132 more rows

Plotting the data

Now our data is ready to be plotted. Let’s create a beautiful dot plot in ggplot using data from GDP per capita, life expectancy, population and continent.

ggplot(df, aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  geom_point() +
  theme_bw()

Interactive Charts

Ok … the graph was beautiful, but so what? How to make it interactive?

This is the easiest part of all! We will simply put the graph we created inside an object and load it using the ggplotly function.

gg = ggplot(df, aes(gdpPercap, lifeExp, size = pop, color=continent)) +
  geom_point() +
  theme_bw()

ggplotly(gg)

Of course, there are many more functions and possibilities using the plotly package. To learn more read the package documentation and visit the plotly website (https://plotly.com/).

Make good use of this information!